Purpose: This study explored whether receiving information about lung cancer screening (LCS) and completing a values clarification exercise affects decisional conflict regarding LCS among individuals with a significant history of cigarette smoking.
Method: Participants were drawn from the Knowledge Networks panel. Of 223 eligible respondents, 210 (94%) consented and participated. Participants had a high risk of lung cancer (40±20 pack years) and were an average age of 61 (±8) years. The sample included 109 (52%) women, 51 (24%) African Americans, and 59 (28%) Hispanic Americans. Prior to receiving a brief description of LCS and completing the conjoint exercise, participations were administered the 10-item low literacy version of the Decisional Conflict Scale (DCS-LL). The brief LCS description provided information regarding options and potential risks/benefits. The conjoint exercise, which was used for values clarification, included 20 scenarios depicting 5 attributes and 17 levels. Participants were asked to respond to each scenario regarding how likely it was that s/he would be screened using response options that ranged from 1 (definitely would not get screened) to 9 (definitely would get screened). Additionally, participants completed 20 survey items that asked them to rate the importance of LCS attributes on a screening decision using a 1-10 scale. Participants then completed the DCS-LL again.
Result: At baseline, participants reported a high level of decisional conflict regarding LCS (M=46.96±27.03, Range: 0 to 100). However, decisional conflict was significantly reduced following the brief LCS introduction and the conjoint exercise (M=17.55±21.40: Range 0 to 100), t(192)=15.54, p<.001, d=1.14. Examination of change in DCS subscales also demonstrated significant differences across all four subscales: uncertainty t(192)=10.06, p<.001, d=.73, informed t(192)=15.99, p<.001, d=1.17, values clarity t(192)=11.78, p<.001, d=.86, and support t(192)=9.26, p<.001, d=.68.
Conclusion: These data suggest that individuals at high risk for lung cancer were generally unprepared to make informed decisions about LCS, but brief educational material combined with a values clarification exercise dramatically reduced decisional conflict. These data support the value of developing a patient decision aid to promote informed decision making about LCS. Future work is needed to design and evaluate a patient decision aid that integrates a risk assessment tool and promotes shared decision making with health care providers.
Purpose: People frequently make choices that are at odds with their stated values. This study tested whether interactive, dynamic online values clarification exercises that explicitly showed 1) tradeoffs inherent in a decision and 2) fit between expressed values and possible options could help people make treatment choices more in line with their stated values.
Method: We conducted a between-subjects online randomized experiment in a demographically diverse population (n=2033, 46% male, 82% white, age range 18-68, 57% no college degree.) We first asked participants: if they had to choose, would they rather die or have a colostomy? Participants were then asked to imagine that they had been diagnosed with colon cancer and faced a choice between two surgeries differing only in that one had a 4% chance of colostomy while the other had a 4% additional chance of death. Participants in the control group proceeded immediately to the surgery choice; other participants interacted with one of four versions of a values clarification exercise. All four versions had two sliders, one labeled, "avoiding colostomy," the other, "avoiding death." Participants moved the sliders to express how much they valued each outcome. Exercises showed tradeoffs, fit, both, or neither. To show tradeoffs, as the participant moved one of the two sliders, the other slider automatically moved equally in the opposite direction. Without this constraint, each slider moved independently of the other. To show fit, we presented two dynamic vertical bars modeling a simple linear relationship between the surgeries and the participant's slider settings. As the user moved the sliders, the vertical bars moved in relation to the sliders. The relationship between sliders and vertical bars was emphasized by matching color cues. (See figure.)
Result: Consistent with our prior research, in the control arm, 22% of people made surgery choices that were discordant with their previously stated values. After interacting with a values clarification exercise that showed neither tradeoffs nor fit, discordance was 23%. Showing tradeoffs reduced discordance to 17%, showing fit reduced it to 18%, and showing both together lowered discordance to 14% (Chi-squared (4) = 13.90, p = .003).
Conclusion: Explicitly showing the tradeoffs inherent in a decision and the fit between values and options can help people make choices more in line with their stated values. 
Purpose: To inform advance directive decisions for patients with severe COPD by comparing probable care trajectories
Methods: We designed a Markov model of patients with severe COPD hospitalized for acute respiratory failure, to estimate the probable trajectories resulting from two alternative advance directives, Do Not Intubate (DNI, no invasive mechanical ventilation) vs. Full Code (all treatments permitted, including invasive mechanical ventilation). We included 5 Markov states: hospitalized with acute respiratory failure; living in the community; living in long-term care extended care facilities (long-term ECF); living in a short term ECF and dead. Outcome measures were 1-year survival, place of discharge, number of re-hospitalizations and a proxy for place of death. Variable estimates were based on published data or expert opinion. Homogeneous data (Q-statistic of >0.10, I-statistic of <25% and p-value <0.05, with no significant outliers on Forest plot) were pooled using Dersimonian and Laird random effects model. One-way and multi-way probabilistic sensitivity analyses were performed to test the model’s robustness and to identify influential variables.
Results: Patients endorsing the Full Code directive had marginally increased 1-year survival (Full Code vs. DNI, 46% vs. 43%). However, Full Code patients were more likely to be residing in a long-term ECF (Full Code vs. DNI, 15% vs. 4%) and to be re-hospitalized (DNI vs. Full Code, 48% vs. 39%). Full Code patients were also more likely to die while living in a long-term ECF (Full Code vs. DNI, 14% vs. 1%). Trajectories were sensitive to the probability of complications of invasive mechanical ventilation and the probability of failing non-invasive mechanical ventilation.
Conclusions: Choosing a Full Code directive may result in a tradeoff between survival versus increased likelihood of recurrent hospitalizations and institutionalization. Making these alternate care trajectories explicit using modeling may better inform advance directive choices for patients with severe COPD.
Purpose: To measure the spillover effects on HRQOL of having a family member with a chronic illness using direct health utility assessment methods.
Method: Using a national sample of US adults, we conducted two cross-sectional surveys in December 2011-January 2012: one version that asked respondents to value hypothetical health states describing the experience of having a family member with a chronic illness (community sample) and one version that asked respondents to value their own experience as the family member of a person with a chronic illness (experienced sample). Chronic illnesses in the survey included Alzheimer’s disease/dementia, arthritis, cancer, cerebral palsy, and depression. Specific illness included in each survey depended on the age of the hypothetical ill individual (child, adult, senior). Respondents for the experienced sample were identified as having a household member with one of these conditions. Using standard gamble questions, respondents were asked to value the spillover effects of a family member’s illness for either hypothetical vignettes or for their own experience as a family member of an ill individual. Disutility is defined as the loss in utility. We used regression analysis to evaluate the disutility associated with having a family member with a chronic illness varied by condition or type of relationship controlling for respondent’s own conditions and sociodemographic characteristics. For the community sample, we also adjusted for multiple observations per respondent.
Result: For the community sample (n=1205), median (95th % CI) spillover disutilities ranged from 0.15 (0.12, 0.25) for cerebral palsy to 0.20 (0.17, 0.26) for cancer. Regression analyses indicated that higher spillover disutility was associated with type of relationship (spouse), lower socioeconomic status, and caregiver experience for the community sample. For the experienced sample (n=1389), median spillover disutilities ranged from 0.06 (0.001, 0.51) for cerebral palsy to 0.27 (0.12, 0.39) for cancer. Regression analyses also suggested higher spillover disutility was associated with lower socioeconomic status but not with type of relationship for the experienced sample.
Conclusion: The effects of illness extend beyond the individual patient to include effects on caregivers of patients, parents of ill children, spouses, and other close family and household members. Cost-effectiveness analyses should consider the inclusion of HRQOL spillover effects in addition to caregiving time costs incurred by family members of ill individuals.
Purpose: A frequently used valuation method for health state valuation is the time trade- off (TTO) method. Typically, valuation studies control for individual characteristics focusing on demographic variables like age, sex, education, and geography. We hypothesized that valuation of hypothetical health states are more prone to variance along other individual variables, including personality, beliefs, attitudes, and personal experience. The purpose of the study was to compare the impact of typical demographic variables to the impact of candidate variables from these other domains.
Method: 511 respondents participated in a web survey. The participants were fairly representative for the Norwegian population with respect to age and sex. Each participant valued eight health states of varying severity as described with the EQ-5D system. Additionally we asked questions about factors we hypothesized could affect their general willingness to trade away time: Agreement with euthanasia, number of children, the personality trait neuroticism, and the extent to which they considered themselves to be religious, to which extent they believed in a life after death. In a multivariate regression we used the TTO value as dependent variable and demographic variables and the other factors with potential influence as independent variables.
Result:
| Linear regression of TTO scores on individual variables | ||||||
| Coeff | Beta | p | ||||
| Intercept | 0.299 | <.001 | ||||
| sex (1 = female) | -0.02 | -0.019 | 0.322 | |||
| age (years) | -0.001 | -0.021 | 0.314 | |||
| 9-12 years of education | -0.046 | -0.04 | 0.28 | |||
| >12 years of education | -0.024 | -0.022 | 0.554 | |||
| Marital status (single vs. attached) | -0.014 | -0.012 | 0.561 | |||
| Children under 18 (dummy) | 0.048 | 0.043 | 0.036 | |||
| Belief in life after death (dummy) | 0.01 | 0.02 | 0.326 | |||
| Religiousity (5 point scale of agreement) | 0.001 | 0.001 | 0.947 | |||
| Attitudes toward euthanasia (mean of three 5 point scales) | -0.074 | -0.141 | <.001 | |||
| Neuroticism (normalized Z scores) | -0.028 | -0.049 | 0.012 |
Conclusion: Typical demographic variables did not significantly influence TTO values. However, having children in the home, attitudes toward euthanasia, and the personality trait neuroticism appear to significantly influence valuation of hypothetical health states. These variables were selected from their respective domains as likely candidates, and suggest that valuation of health states may be informed more by attitudes, personality, and experiences than the usual demographic variables. Variable relevance should be carefully considered.
Purpose: In a study of men undergoing biopsy and treatment for prostate cancer, we examined whether pre-existing Cancer Anxiety and preferences for active medical interventions (Action Bias) influence treatment preferences and decisions. We used an established measure of Anxiety and a new measure of Action Bias to explore how these pre-existing individual differences impact decisions at different points in the decision-making process.
Method: 1015 men, with suspicion of prostate cancer, were recruited from 4 VA hospitals at the time of biopsy, as a part of a study of prostate cancer decision aids (DA). Prior to reading a DA, patients completed a questionnaire that assessed their prostate cancer anxiety (Memorial Anxiety Scale for Prostate Cancer), and their bias toward active treatment options (e.g. “Doing everything to fight cancer is the right choice”). These baseline measures were used to predict (1) treatment preferences expressed after reading the DA, but prior to diagnosis, (2) treatment decisions following the urologist visit for diagnosis, and (3) treatment receivedaccording to medical records.
Result: For preferences expressed prior to diagnosis, patients who preferred surgery had greater pre-existing Action Bias than those who did not (M=6.40 vs. 6.02, p<.01) and patients who preferred active surveillance had less Action Bias than those who did not (M=5.85 vs. 6.34, p<.01). Anxiety was not predictive. Later, after prostate cancer was diagnosed, both Action Bias and Anxiety predicted treatment decisions among patients who had definitively decided upon a course of action: Those who selected active surveillance had less Action Bias (M=5.81) and Anxiety (M=.64) than those who selected surgery (M(action)=6.50; M(anxiety)=1.12) or radiation (M(action)=6.64; M(anxiety)=1.31; all p<.01). Finally, patients who actually received surgery had greater pre-existing Anxiety (M=1.09) than those who received active surveillance (M=.80), but this difference did not reach significance (p=.07). Action Bias was not predictive of treatment received (p=.54).
Conclusion: Prior to diagnosis, patients’ treatment preferences were related to Action Bias but not Anxiety. After diagnosis, treatment decisions were related to both Action Bias and Anxiety. Finally, treatment received was marginally related to Anxiety but not Action Bias. Together these findings reveal that relatively uninformed, preexisting individual differences can play a significant role in treatment decision-making, and that these factors may have varying degrees of impact at different points in the decision making process.